How we work

Start small. Prove value. Scale.

Three ways to engage, one measure-first approach, and no long lock-ins.

01 · Engagement models

Three ways to engage.

2–3 weeks

Architecture Sprint

A fixed-scope assessment of your idea or existing system.

  • Stakeholder & data workshops
  • Target architecture & risks
  • Prioritised roadmap with estimates

Fixed price

6–12 weeks

Build Squad

A small senior team that takes one use case to production.

  • Architect + AI engineers
  • Weekly demos, eval dashboards
  • Hand-over, docs & runbooks

Milestone-based

Ongoing

Embedded Experts

AI engineers or a fractional architect inside your team.

  • Flexible capacity
  • Works in your tools & rituals
  • Scale up or down monthly

Time & materials

Need something in between, or a different setup? Engagements are tailored to you. Tell us what you need →

02 · Starting point

Find your starting point in 3 clicks.

Not sure which engagement fits? Answer three short questions and we'll suggest where to begin.

03 · Approach

Measure first. Build second. Scale last.

Step 01 · Discover

  • Stakeholder interviews & process walk-through
  • Data and systems audit
  • Success metrics and first eval set
  • Risk and compliance screening

Vendor-neutral

No reseller deals. We recommend what fits: open models, hosted APIs, or no AI at all.

Senior by default

The people you meet in the first call are the people doing the work.

You own everything

Code, prompts, models, evals and docs live in your repositories from day one.

04 · FAQ

Common questions.

We don't have an AI strategy yet. Is it too early?

No, that's the best time. An Architecture Sprint helps you avoid expensive dead ends and pick the use cases worth building first.

Can you work with our existing cloud and security policies?

Yes. We design within your constraints, AWS, Azure or GCP, private networking, data residency in the EU, and your identity and access model.

Will our data be used to train third-party models?

Not unless you decide so. We default to setups where your data stays in your tenant and provider data-retention is disabled.

Do you only work with large enterprises?

No. We work with startups, scale-ups and enterprises. Engagements are sized to the problem, not the logo.

Do you work with both hosted APIs and open-source models?

Yes. We are vendor-neutral: Anthropic, OpenAI, Google, Mistral and AWS/Azure/GCP model services, as well as open models (Llama, Qwen, Mistral, Gemma) self-hosted in your cloud. We choose based on your evals, data constraints and cost.

Can you build an MCP server for our product or internal systems?

Yes. We design agent-friendly tools, implement remote MCP servers with OAuth and scopes, and add a registry and gateway if you run many of them.

Which data platform or cloud should we choose?

It depends on your existing cloud, team skills and workloads, see the comparison on our Platforms & Cloud page. We are vendor-neutral and often run a short proof-of-concept on your real workloads before recommending one.

Can you migrate our AI workloads to another cloud provider?

Yes, for example Azure OpenAI to AWS Bedrock, Vertex AI to Azure, or APIs to self-hosted models. We add an abstraction layer, evaluate old and new side by side and cut over in phases with rollback.

How do contracts and billing work?

Fixed price for sprints, milestone-based for build projects, and monthly time & materials for embedded experts. B2B contracts across the EU and beyond.

Next step

Let's talk about your AI system.

A free 30-minute call with an Engagement Lead or AI Architect. You'll leave with a clearer view of options, risks and cost, whether or not we work together. Your case doesn't need to fit any box on this site; just tell us what you're facing.

Book a free call Find your starting point

Keep exploring

There’s more to see.

Up next · About Hands-on engineering, not slideware Who we are and the principles we work by. Blog Notes from the field In-depth articles on architecture, production lessons and research. Services Everything between the idea and the SLA Agents, MCP servers, RAG, fine-tuning, voice, vision and governance, 19 hands-on capabilities. Platforms & Cloud The heavy lifting behind AI Data & AI platforms, landing zones, accelerators and migrations between cloud AI providers. GPU Compute Your models. Your region. Your GPUs. Dedicated, in-region GPU capacity to host, fine-tune and train your own models.